Knowledge Crystallization
Card, Mackinlay, Shneiderman (1999), “Information Visualization”, Introduction to “Readings in Information Visualization: Using Vision to Think”
Knowledge crystallization
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The process of:
– wanting to find out some information (your task)
– looking at all the data you can find relating to the task
– creating a schema/framework to describe the data
– packaging it so that it can be communicated to
– … and using it to solve your task
Sue is asked by her colleagues for a laptop recommendation
She gathers a whole load of information from vendors’ websites, brochures, friends and colleagues, trade magazines…
… and, in doing so, identifies several attributes (processor speed, weight, screen size etc.)
She creates a table: rows are different products; columns are attributes (the ‘schema’). In doing so, she realises
some attributes are missing, some attributes need to be separated into two, some models have been discontinued etc., some attributes are irrelevant … so she changes the schema
some information is missing… so she seeks it out, and adds it
In doing so, she realises that some attributes are related to each other (weight, screen size); some are trade-offs (cost, processing speed)… so she changes the schema column order to highlight these
She can then present this information to her colleagues in a way that shows the insight that she herself has gained during this process
The Knowledge Crystallization process, CMS p10, edited
Sue is asked by her colleagues for a laptop recommendation
She gathers a whole load of information from vendors’ websites, brochures, friends and colleagues, trade magazines…
… and, in doing so, identifies several attributes (processor speed, weight, screen size etc.)
She creates a table: columns are different products; rows are attributes (the ‘schema’). In doing so, she realises
some attributes are missing, some attributes need to be separated into two, some models have been discontinued etc., some attributes are irrelevant … so she changes the schema
some information is missing… so she seeks it out, and adds it
In doing so, she realises that some attributes are related to each other (weight, screen size); some are trade-offs (cost, processing speed)… so she changes the schema column order to highlight these
She can then present this information to her colleagues in a way that shows the insight that she herself has gained during this process
The Knowledge Crystallization process, CMS p10, edited
Sue is asked by her colleagues for a laptop recommendation
She gathers a whole load of information from vendors’ websites, brochures, friends and colleagues, trade magazines…
… and, in doing so, identifies several attributes (processor speed, weight, screen size etc.)
She creates a table: columns are different products; rows are attributes (the ‘schema’). In doing so, she realises
some attributes are missing, some attributes need to be separated into two, some models have been discontinued etc., some attributes are irrelevant … so she changes the schema
some information is missing… so she seeks it out, and adds it
In doing so, she realises that some attributes are related to each other (weight, screen size); some are trade-offs (cost, processing speed)… so she changes the schema row order to highlight these relationships
She can then present this information to her colleagues in a way that shows the insight that she herself has gained during this process
The Knowledge Crystallization process, CMS p10, edited
Sue is asked by her colleagues for a laptop recommendation
She gathers a whole load of information from vendors’ websites, brochures, friends and colleagues, trade magazines…
… and, in doing so, identifies several attributes (processor speed, weight, screen size etc.)
She creates a table: columns are different products; rows are attributes (the ‘schema’). In doing so, she realises
some attributes are missing, some attributes need to be separated into two, some models have been discontinued etc., some attributes are irrelevant … so she changes the schema
some information is missing… so she seeks it out, and adds it
In doing so, she realises that some attributes are related to each other (weight, screen size); some are trade-offs (cost, processing speed)… so she changes the schema row order to highlight these relationships
She can then present this information to her colleagues in a way that
shows the insight that she herself has gained during this process
The Knowledge Crystallization process, CMS p10, edited
Inspiron HUAWEI MateBook D HP 15-dw1004na Lenovo V15 ADA Lenovo IdeaPad Lenovo V15 ADA Black 15.6″ Full HD Laptop
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Computer Memory 8 GB 8 GB 8 GB 8 GB 8 GB 8 GB Size
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CPU Model Ryzen 5 3500U Ryzen 5 3500U Core i3 Athlon Core i5 Family R Series
CPU Model AMD AMD Intel AMD Intel AMD Manufacturer
CPU Speed 3.7 GHz 3.4 2.1 GHz 2.4 GHz 1 GHz 2.6 GHz
Item Weight 2 kg 1.53 kg 2 kg 0 grammes 1.7 kg 2.1 kg
Screen Size 15.6 in 15.6 in 15.6 in 15.6 in 15.6 in 15.6 in
Display Technology LED — — — — —
Resolution 1080p 1920 x 1080 1080p 1920 x 1080 1920 x 1080 1920 x 1080
Hard Disk Description Flash Memory Solid Flash Memory Solid Flash Memory Solid SSD SSD SSD State State State
Hard Disk Size 256 GB 256 GB — 256 GB 256 GB 256 GB
Operating System Windows 10 Windows 10 Home Windows 10 Home Windows 10 Home Windows 10 S Windows 10 Home
Processor Count 1 1 2 2 4 2
Processor Description AMD Ryzen AMD R5, Win 10 Intel Core AMD Athlon Intel Core i5 10th Gen AMD Ryzen Home
RAM Type DDR4 SDRAM DDR4 SDRAM DDR4 SDRAM DDR4 SDRAM DDR4 SDRAM DDR SDRAM
https://www.amazon.co.uk/dp/B08NQ1VX6Z/ (08/07/21)
Inspiron Black HUAWEI MateBook D HP 15-dw1004na Lenovo V15 ADA Lenovo V15 ADA 15.6″ Full HD
Price £524.00 £599.99 £435.23 £399.00 £524.40
Sold By COMPSOLUK Amazon.co.uk Amazon Warehouse MESH Computers Techno world Plc
Computer Memory Size 8 GB 8 GB 8 GB 8 GB 8 GB
RAM Type DDR4 SDRAM DDR4 SDRAM DDR4 SDRAM DDR4 SDRAM DDR SDRAM
Operating System Windows 10 Windows 10 Home Windows 10 Home Windows 10 Windows 10 Home Home
Connectivity Technology Bluetooth; USB; HDMI; Wi-Fi Bluetooth; Wi-Fi; USB Bluetooth; Ethernet; — Wi-Fi HDMI; USB; Wi-Fi
CPU Model Ryzen 5 3500U Ryzen 5 3500U Core i3 Athlon R Series
CPU Model Manufacturer AMD AMD Intel AMD AMD
CPU Speed 3.7 GHz 3.4 2.1 GHz 2.4 GHz 2.6 GHz
Item Weight 2 kg 1.53 kg 2 kg 0 grammes 2.1 kg
Screen Size 15.6 in 15.6 in 15.6 in 15.6 in 15.6 in
Display Technology LED — — — —
Resolution 1080p 1920 x 1080 1080p 1920 x 1080 1920 x 1080
Hard Disk Description Flash Memory Solid State Flash Memory Solid Flash Memory Solid SSD SSD State State
Hard Disk Size 256 GB 256 GB — 256 GB 256 GB
Processor Count 1 1 2 2 2
Processor Description AMD Ryzen AMD R5, Win 10 Intel Core AMD Athlon AMD Ryzen Home
https://www.amazon.co.uk/dp/B08NQ1VX6Z/ (08/07/21)
John needs to decide which hill races to enter this summer
He gathers all the information he can find about all the summer races, with their attributes (length, climb, record time, cost)
…. and creates a schema of rows (races), and columns (attributes)
In doing so, he discovers new attributes (e.g. point-to-point vs loop, date, registration process), some races include more than one distance, different costs for club members etc. He changes his schema accordingly.
He also identifies races that are too long/short for his interest, and removes them, and he seeks out information that he does not have.
He sees some races are held the same day, or are so close together there is insufficient time to recover from one race before the next
He realises that the race dates are an important attribute, and so changes his schema to a timetable representation, where the number of days between each race is clearly seen
This makes it easier to find a sensible schedule through the summer that identifies the races he will take part in
John needs to decide which hill races to enter this summer
He gathers all the information he can find about all the summer races, with their attributes (length, climb, record time, cost)
…. and creates a schema of rows (races) and columns (attributes)
In doing so, he discovers new attributes (e.g. point-to-point vs loop, date, registration process), some races include more than one distance, different costs for club members etc. He changes his schema accordingly.
He also identifies races that are too long/short for his interest, and removes them, and he seeks out information that he does not have.
He sees some races are held the same day, or are so close together there is insufficient time to recover from one race before the next
He realises that the race dates are an important attribute, and so changes his schema to a timetable representation, where the number of days between each race is clearly seen
This makes it easier to find a sensible schedule through the summer that identifies the races he will take part in
John needs to decide which hill races to enter this summer
He gathers all the information he can find about all the summer races, with their attributes (length, climb, record time, cost)
…. and creates a schema of rows (races) and columns (attributes)
In doing so, he discovers new attributes (e.g. point-to-point vs loop, date, registration process), some races include more than one distance, different costs for club members etc. He changes his schema accordingly.
He also identifies races that are too long/short for his interest, and removes them, and he seeks out information that he does not have.
He sees some races are held the same day, or are so close together there is insufficient time to recover from one race before the next
He realises that the race dates are an important attribute, and so changes his schema to a timetable representation, where the number of days between each race is clearly seen
This makes it easier to find a sensible schedule through the summer that identifies the races he will take part in
John needs to decide which hill races to enter this summer
He gathers all the information he can find about all the summer races, with their attributes (length, climb, record time, cost)
…. and creates a schema of rows (races) columns (attributes)
In doing so, he discovers new attributes (e.g. point-to-point vs loop, date, registration process), some races include more than one distance, different costs for club members etc. He changes his schema accordingly.
He also identifies races that are too long/short for his interest, and removes them, and he seeks out information that he does not have.
He sees some races are held the same day, or are so close together there is insufficient time to recover from one race before the next
He realises that the race dates are an important attribute, and so changes his schema to a timetable representation, where the number of days between each race is clearly seen
This makes it easier to find a sensible schedule through the summer that identifies the races he will take part in.
John’s first schema
John’s second schema
A (30km,220m)
D (32km,110m)
Ea (5km,80m) Eb (10km,160m)
F (20km,530m) B (12km,610m)
C (5km, 650m)
John’s third schema
The steps of Knowledge Crystallization
1. Forage for information/data
2. Create schema – the framework for holding the information
3. Instantiate the schema by putting the information in it
a. solve the problems that emerge: change or extend schema, add or remove information
b. manipulate the schema to reveal insight about the information gathered along the way
c. repeat until the most effective and efficient schema is found
4. Present the information to others, using the best schema
5. Use it to solve your task
Requirements for Knowledge Crystallization: data, task, existing or potential schema
CMS p10, edited
Knowledge crystallisation
Information Visualisation can support all stages
CMS p10, edited
Knowledge Crystallization
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